Syntactic learning curves in LMs are usually reported as relatively stable and power law-shaped. By analyzing the learning curves of different LMs on various syntactic phenomena using both small self-trained llama models and larger pre-trained pythia models, we show that while many phenomena do follow typical power law curves, others exhibit S-shaped, U-shaped, or erratic patterns. Certain syntactic paradigms remain challenging even for large models, resulting in persistent preference for ungrammatical sentences. Most phenomena show similar curves for their paradigms, but the existence of diverging patterns and oscillations indicates that average curves mask important developments, underscoring the need for more detailed analyses of individual learning trajectories.

The success of large language models (LMs) has also prompted a push towards smaller models, but the differences in functionality and encodings between these two types of models are not yet well understood. In this paper, we employ a perturbed masking approach to investigate differences in token influence patterns on the sequence embeddings of larger and smaller RoBERTa models. Specifically, we explore how token properties like position, length or part of speech influence their sequence embeddings. We find that there is a general tendency for sequence-final tokens to exert a higher influence. Among part-of-speech tags, nouns, numerals and punctuation marks are the most influential, with smaller deviations for individual models. These findings also align with usage-based linguistic evidence on the effect of entrenchment. Finally, we show that the relationship between data size and model size influences the variability and brittleness of these effects, hinting towards a need for holistically balanced models.